Machine Learning and Knowledge Discovery in Databases

1. Semi-Supervised and Transductive Learning

   1. Coupled Bayesian Sets Algorithm for Semi-supervised Learning and Information Extraction

      • Saurabh Verma, Estevam R. Hruschka Jr.

      Pages 307-322

   2. Graph-Based Transduction with Confidence

      • Matan Orbach, Koby Crammer

      Pages 323-338

   3. Maximum Consistency Preferential Random Walks

      • Deguang Kong, Chris Ding

      Pages 339-354

   4. Semi-supervised Multi-label Classification

      • Yuhong Guo, Dale Schuurmans

      Pages 355-370

2. Sensor Data

   1. MDL-Based Analysis of Time Series at Multiple Time-Scales

      • Ugo Vespier, Arno Knobbe, Siegfried Nijssen, Joaquin Vanschoren

      Pages 371-386

   2. Separable Approximate Optimization of Support Vector Machines for Distributed Sensing

      • Sangkyun Lee, Marco Stolpe, Katharina Morik

      Pages 387-402

   3. Unsupervised Inference of Auditory Attention from Biosensors

      • Melih Kandemir, Arto Klami, Akos Vetek, Samuel Kaski

      Pages 403-418

3. Sequence and String Mining

   1. A Family of Feed-Forward Models for Protein Sequence Classification

      • Sam Blasiak, Huzefa Rangwala, Kathryn B. Laskey

      Pages 419-434

   2. General Algorithms for Mining Closed Flexible Patterns under Various Equivalence Relations

      • Tomohiro I, Yuki Enokuma, Hideo Bannai, Masayuki Takeda

      Pages 435-450

   3. Size Matters: Finding the Most Informative Set of Window Lengths

      • Jefrey Lijffijt, Panagiotis Papapetrou, Kai Puolamäki

      Pages 451-466

4. Social Network Mining

   1. Discovering Links among Social Networks

      • Francesco Buccafurri, Gianluca Lax, Antonino Nocera, Domenico Ursino

      Pages 467-482

   2. Efficient Bi-objective Team Formation in Social Networks

      • Mehdi Kargar, Aijun An, Morteza Zihayat

      Pages 483-498

   3. Feature-Enhanced Probabilistic Models for Diffusion Network Inference

      • Liaoruo Wang, Stefano Ermon, John E. Hopcroft

      Pages 499-514

   4. Influence Spread in Large-Scale Social Networks – A Belief Propagation Approach

      • Huy Nguyen, Rong Zheng

      Pages 515-530

   5. Location Affiliation Networks: Bonding Social and Spatial Information

      • Konstantinos Pelechrinis, Prashant Krishnamurthy

      Pages 531-547

   6. On Approximation of Real-World Influence Spread

      • Yu Yang, Enhong Chen, Qi Liu, Biao Xiang, Tong Xu, Shafqat Ali Shad

      Pages 548-564

   7. Opinion Formation by Voter Model with Temporal Decay Dynamics

      • Masahiro Kimura, Kazumi Saito, Kouzou Ohara, Hiroshi Motoda

      Pages 565-580

   8. Viral Marketing for Product Cross-Sell through Social Networks

      • Ramasuri Narayanam, Amit A. Nanavati

      Pages 581-596

   9. Which Topic Will You Follow?

      • Deqing Yang, Yanghua Xiao, Bo Xu, Hanghang Tong, Wei Wang, Sheng Huang

      Pages 597-612

5. Spatial and Geographical Data Mining

   1. Inferring Geographic Coincidence in Ephemeral Social Networks

      • Honglei Zhuang, Alvin Chin, Sen Wu, Wei Wang, Xia Wang, Jie Tang

      Pages 613-628

This two-volume set LNAI 7523 and LNAI 7524 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2012, held in Bristol, UK, in September 2012. The 105 revised research papers presented together with 5 invited talks were carefully reviewed and selected from 443 submissions. The final sections of the proceedings are devoted to Demo and Nectar papers. The Demo track includes 10 papers (from 19 submissions) and the Nectar track includes 4 papers (from 14 submissions). The papers grouped in topical sections on association rules and frequent patterns; Bayesian learning and graphical models; classification; dimensionality reduction, feature selection and extraction; distance-based methods and kernels; ensemble methods; graph and tree mining; large-scale, distributed and parallel mining and learning; multi-relational mining and learning; multi-task learning; natural language processing; online learning and data streams; privacy and security; rankings and recommendations; reinforcement learning and planning; rule mining and subgroup discovery; semi-supervised and transductive learning; sensor data; sequence and string mining; social network mining; spatial and geographical data mining; statistical methods and evaluation; time series and temporal data mining; and transfer learning.

• Bayesian network classifiers
• data mining
• hypergraphs
• social media
• tree search

• Intelligent Systems Laboratory, University of Bristol, Bristol, UK

  Peter A. Flach, Tijl Bie, Nello Cristianini

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